Mobile terminals, which may include any portable and/or mobile electronic device, are currently being developed to provide wireless communication between users. As technology has advanced, mobile terminals now provide many additional features beyond simple telecommunications and voice services. For example, mobile terminals may now provide additional features and services, such as an alarm, a Short Messaging Service (SMS), a Multimedia Message Service (MMS), E-mail, games, remote control of short range communication, an image capturing function using a mounted digital camera, a multimedia function for providing audio and video content, a scheduling function, and many more. With the plurality of features now provided, mobile terminals have become widely used in daily life.
For example, mobile terminals provide an image capturing function, which may include capturing at least one of a 2 Dimensional (2D) image, a 3 Dimensional (3D) image, a video and/or moving image, a still image derived from the video and/or moving, an image derived from sensors and/or sensor data, and/or a multimedia image. The mobile terminals may include a depth sensor in order to detect a depth of an object included in a captured image. Accordingly, the depth sensor may measure a distance to an object in order to provide distance information in addition to standard color information to be included in image data. For example, by augmenting a Joint Pictures Expert Group (JPEG) file format by approximately 30%, depth information may be added to color images. Thus, depth information may be used in a wide variety of applications, such as searching of 3D images, searching of 3D information, creating and/or printing object models, and any other similar and/or suitable application using depth information.
Currently, related art research in improving a performance of depth sensors has been directed towards filtering Gaussian noise, such as thermal noise, “salt and pepper” noise, and any other similar form of noise that may be classified as Gaussian noise, from a 3D image generated according to the depth sensor. In order to remove the Gaussian noise from a 3D image, image denoising methods, which include methods to remove noise from an image, may be used. The image denoising methods of the related art may include averaging, determining a median, and any other similar and/or suitable image denoising methods. However the main effect of applying a filter to reduce Gaussian noise in the related art is additional blur in the case of averaging or blurring of low spatial frequencies in the case of a median filter. Moreover, averaging pixels in a depth map would lead to virtually connecting separate objects during 3D reconstruction and thus is prohibitive.
In the case of a depth sensor, the noise may be classified as non-Gaussian noise, which may also be referred to as “blob-like” noise. Additionally, related-art techniques for determining whether there is noise, and such as taking a derivative of a depth signal generated by a depth sensor, may not consider spatial distribution of noise and/or spatial distribution of object depths. Furthermore, if depth information is contaminated with just a few noisy pixels, then information corresponding to a size and shape of objects sensed by a depth sensor may be significantly distorted. Many depth sensors share the same noise problems due to incorrect matching of stereo images, occlusions, lens distortions, etc. Accordingly, there is a need for an apparatus and method providing an improved method for reducing noise in depth images.
The above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as prior art with regard to the present disclosure.